Grammatical Evolution Guided by Reinforcement

@InProceedings{Mingo:2007:cec,
author = "Jack Mario Mingo and Ricardo Aler",
title = "Grammatical Evolution Guided by Reinforcement",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1475--1482",
address = "Singapore",
month = "25-28 " # sep,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "1-4244-1340-0",
DOI = "doi:10.1109/CEC.2007.4424646",
abstract = "Grammatical Evolution is an evolutionary algorithm
able to develop, starting from a grammar, programs in
any language. Starting from the point that individual
learning can improve evolution, in this paper it is
proposed an extension of Grammatical Evolution that
looks at learning by reinforcement as a learning method
for individuals. This way, it is possible to
incorporate the Baldwinian mechanism to the
evolutionary process. The effect is widened with the
introduction of the Lamarck hypothesis. The system is
tested in two different domains: a symbolic regression
problem and an even parity Boolean function. Results
show that for these domains, a system which includes
learning obtains better results than a grammatical
evolution basic system.",
notes = "Q tree
CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
file = "1738.pdf",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cec/cec2007.html#MingoA07",
}